diff --git a/homework/m1_numpy.py b/homework/m1_numpy.py
index 5ff41ab..7b7b847 100644
--- a/homework/m1_numpy.py
+++ b/homework/m1_numpy.py
@@ -19,19 +19,27 @@
 def green_mean():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素的平均值 (float)"""
     # TODO: 你的程式碼
-    pass
+    arr = (np.arange(5) + 1) * 10
+    return arr.mean().item()
+
+# print(green_mean())
 
 
 def green_double():
     """建立 [10, 20, 30, 40, 50]，回傳所有元素乘以 2 的 ndarray"""
     # TODO: 你的程式碼
-    pass
+    arr = (np.arange(5) + 1) * 10 * 2
+    return arr
 
+# print(green_double())
 
 def green_filter():
     """建立 [10, 20, 30, 40, 50]，回傳大於 25 的元素 (ndarray)"""
     # TODO: 你的程式碼
-    pass
+    arr = (np.arange(5) + 1) * 10
+    return arr[arr>25]
+
+# print(green_filter())
 
 
 # ============================================================
@@ -42,7 +50,7 @@ def green_filter():
 def yellow_expensive_count(prices):
     """回傳單價 > 1000 的商品數量 (int)"""
     # TODO: 你的程式碼
-    pass
+    return int((prices > 1000).sum())
 
 
 def yellow_top3_stock_indices(stocks):
@@ -51,7 +59,7 @@ def yellow_top3_stock_indices(stocks):
     提示：np.argsort
     """
     # TODO: 你的程式碼
-    pass
+    return np.argsort(-stocks)[:3]
 
 
 def yellow_restock_cost(prices, stocks):
@@ -60,8 +68,18 @@ def yellow_restock_cost(prices, stocks):
     提示：布林遮罩 + .sum()
     """
     # TODO: 你的程式碼
-    pass
-
+    mask = prices<500
+    return sum(50 * prices[mask])
+
+# import pandas as pd
+# C:\python_data\python-da-homework-2026\datasets\ecommerce\products.csv
+# df = pd.read_csv('../datasets/ecommerce/products.csv')
+# print(df['unit_price'])
+# print(df['stock_qty'])
+# print(yellow_expensive_count(df['unit_price']))
+# print(type(yellow_expensive_count(df['unit_price'])))
+# print(yellow_top3_stock_indices(df['stock_qty']))
+# print(yellow_restock_cost(df['unit_price'], df['stock_qty']))
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -77,4 +95,11 @@ def red_double11_prices(prices, stocks):
     提示：np.where 可以巢狀使用
     """
     # TODO: 你的程式碼
-    pass
+    # print(prices, stocks)
+    new_prices = np.where(stocks>=100, prices*0.7, np.where(stocks>=20, prices*0.9, prices))
+    # print(type(new_prices))
+    return new_prices
+
+# import pandas as pd
+# df = pd.read_csv('../datasets/ecommerce/products.csv')
+# print(red_double11_prices(df['unit_price'], df['stock_qty']))
\ No newline at end of file
diff --git a/homework/m2_pandas_cleaning.py b/homework/m2_pandas_cleaning.py
index fa36bff..f676c3f 100644
--- a/homework/m2_pandas_cleaning.py
+++ b/homework/m2_pandas_cleaning.py
@@ -20,7 +20,9 @@ def green_read_csv():
     提示：pd.read_csv()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv('datasets/ecommerce/orders_raw.csv')
+    # print(df.head())
+    return df
 
 
 def green_shape(df):
@@ -29,7 +31,7 @@ def green_shape(df):
     提示：df.shape
     """
     # TODO: 你的程式碼
-    pass
+    return df.shape
 
 
 def green_dtypes(df):
@@ -38,8 +40,12 @@ def green_dtypes(df):
     提示：df.dtypes
     """
     # TODO: 你的程式碼
-    pass
+    return df.dtypes
 
+# temp = green_read_csv()
+# print(green_read_csv())
+# print(green_shape(temp))
+# print(green_dtypes(temp))
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
@@ -52,7 +58,9 @@ def yellow_clean_columns(df):
     提示：df.columns.str.strip().str.lower()
     """
     # TODO: 你的程式碼
-    pass
+    df2 = df.copy()
+    df2.columns = df2.columns.str.strip().str.lower()
+    return df2
 
 
 def yellow_clean_amount(df):
@@ -63,7 +71,9 @@ def yellow_clean_amount(df):
     提示：.str.replace() + .astype(float)
     """
     # TODO: 你的程式碼
-    pass
+    df2 = df.copy()
+    df2['amount'] = df2['amount'].str.replace('$', '').str.replace(',', '').astype(float)
+    return df2
 
 
 def yellow_drop_duplicates(df):
@@ -72,9 +82,12 @@ def yellow_drop_duplicates(df):
     提示：df.drop_duplicates()
     """
     # TODO: 你的程式碼
-    pass
-
+    df2 = df.drop_duplicates()
+    return df2
 
+# print(yellow_clean_columns(temp))
+# print(yellow_clean_amount(temp))
+# print(yellow_drop_duplicates(temp))
 # ============================================================
 # 🔴 挑戰題（25 分）
 # ============================================================
@@ -93,4 +106,15 @@ def red_clean_orders(path):
     提示：pd.to_datetime(errors='coerce')
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv(path)
+    df.columns = df.columns.str.strip().str.lower()
+    df['amount'] = df['amount'].str.replace('$', '').str.replace(',', '').astype(float)
+    df['order_date'] = pd.to_datetime(df['order_date'], errors='coerce')
+    df = df.dropna(subset=['order_date', 'amount'])
+    df = df.drop_duplicates()
+
+    return df
+
+# print(red_clean_orders('../datasets/ecommerce/orders_raw.csv').head())
+
+# print(red_clean_orders('../datasets/ecommerce/orders_raw.csv').shape)
diff --git a/homework/m3_pandas_advanced.py b/homework/m3_pandas_advanced.py
index 68410e8..7a264fe 100644
--- a/homework/m3_pandas_advanced.py
+++ b/homework/m3_pandas_advanced.py
@@ -24,21 +24,41 @@ def green_load_and_merge():
     提示：pd.merge(how='left')
     """
     # TODO: 你的程式碼
-    pass
+    df1 = pd.read_csv('datasets/ecommerce/orders_clean.csv')
+    df2 = pd.read_csv('datasets/ecommerce/customers.csv')
+    df3 = pd.read_csv('datasets/ecommerce/products.csv')
+
+    df = (
+        df1
+        .merge(df2, on='customer_id', how='left')
+        .merge(df3, on='product_id', how='left')
+    )
+
+    # print(df.head())
+    # print(df.shape)
+    # print(df.columns)
+    
+    return df
 
 
 def green_row_count(df):
     """回傳 DataFrame 的列數 (int)"""
     # TODO: 你的程式碼
-    pass
+    # print(len(df))
+    return len(df)
 
 
 def green_column_list(df):
     """回傳 DataFrame 的所有欄位名稱 (list)"""
     # TODO: 你的程式碼
-    pass
+    return list(df.columns)
 
 
+# temp = green_load_and_merge()
+# leng = green_row_count(temp)
+# leng = green_column_list(temp)
+# print(leng)
+
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
 # ============================================================
@@ -50,7 +70,9 @@ def yellow_top_category(df):
     提示：groupby('category')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    product = df.groupby('category')['amount'].sum().idxmax()
+    # print(product)
+    return product
 
 
 def yellow_gold_vip_stats(df):
@@ -60,7 +82,9 @@ def yellow_gold_vip_stats(df):
     提示：df[df['vip_level'] == 'Gold']
     """
     # TODO: 你的程式碼
-    pass
+    order_nums = df[df['vip_level'] == 'Gold']['order_id'].count()
+    order_sum = df[df['vip_level'] == 'Gold']['amount'].sum()
+    return (order_nums, order_sum)
 
 
 def yellow_region_avg_amount(df):
@@ -70,7 +94,15 @@ def yellow_region_avg_amount(df):
     提示：groupby('region')['amount'].mean()
     """
     # TODO: 你的程式碼
-    pass
+    avg_money = df.groupby('region')['amount'].mean()
+    # print(avg_money)
+    # print(type(avg_money))
+    return avg_money
+
+# temp = green_load_and_merge()
+# tmp = yellow_top_category(temp)
+# tmp = yellow_gold_vip_stats(temp)
+# tmp = yellow_region_avg_amount(temp)
 
 
 # ============================================================
@@ -94,4 +126,21 @@ def red_rfm_top5(df):
     提示：groupby('customer_id').agg(...)
     """
     # TODO: 你的程式碼
-    pass
+    rfm = df.groupby('customer_id').agg(
+        R = ('order_date', 'max'),
+        F = ('order_id', 'count'),
+        M = ('amount', 'sum')
+    )
+
+    cus_name = df[['customer_id', 'customer_name']].drop_duplicates()
+
+    rfm = rfm.merge(
+        cus_name,
+        on= 'customer_id',
+        how='right'
+    ).sort_values('M', ascending=False)
+    rfm = rfm.head(5).reset_index()[['customer_id', 'customer_name', 'R', 'F', 'M']]
+    # print(rfm)
+    return rfm
+
+# tmp = red_rfm_top5(temp)
\ No newline at end of file
diff --git a/homework/m4_timeseries.py b/homework/m4_timeseries.py
index 047af6a..8a7fe88 100644
--- a/homework/m4_timeseries.py
+++ b/homework/m4_timeseries.py
@@ -27,8 +27,9 @@ def green_avg_by_month():
     提示：df['order_date'].dt.month
     """
     # TODO: 你的程式碼
-    pass
-
+    df = _load_data()
+    ans = df.groupby(df['order_date'].dt.month)['amount'].mean()
+    return ans
 
 def green_top3_dates():
     """
@@ -37,7 +38,9 @@ def green_top3_dates():
     提示：value_counts().head(3)
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    ans = df['order_date'].dt.date.value_counts().head(3)
+    return ans
 
 
 def green_date_range():
@@ -46,7 +49,10 @@ def green_date_range():
     格式為 pandas Timestamp
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    # temp = (df['order_date'].min(), df['order_date'].max())
+    # print(type(temp))
+    return (df['order_date'].min(), df['order_date'].max())
 
 
 # ============================================================
@@ -60,7 +66,8 @@ def yellow_monthly_revenue():
     提示：set_index('order_date').resample('ME')['amount'].sum()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    return df.set_index('order_date').resample('ME')['amount'].sum()
 
 
 def yellow_rolling_avg(monthly_revenue):
@@ -71,7 +78,7 @@ def yellow_rolling_avg(monthly_revenue):
     提示：.rolling(window=3).mean()
     """
     # TODO: 你的程式碼
-    pass
+    return monthly_revenue.rolling(window=3).mean()
 
 
 def yellow_category_median(df):
@@ -81,8 +88,16 @@ def yellow_category_median(df):
     提示：groupby + median + sort_values
     """
     # TODO: 你的程式碼
-    pass
+    return df.groupby('category')['amount'].median().sort_values(ascending=False)
 
+# tmp = _load_data()
+# tmp = green_avg_by_month()
+# tmp = green_top3_dates()
+# tmp = green_date_range()
+# tmp = yellow_monthly_revenue()
+# tmp = yellow_rolling_avg(tmp)
+# tmp = yellow_category_median(_load_data())
+# print(tmp)
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -101,4 +116,15 @@ def red_monthly_report():
     提示：resample + agg + pct_change
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    ans = df.groupby(df['order_date'].dt.to_period('M')).agg(
+        order_count= ('order_id', 'count'),
+        revenue= ('amount', 'sum'),
+        active_customers= ('customer_id', 'nunique'),
+    ).sort_index()
+
+    ans['avg_order_value'] = ans['revenue'] / ans['order_count']
+    ans['revenue_growth'] = ans['revenue'].pct_change() * 100
+    return ans
+
+# print(red_monthly_report())
\ No newline at end of file
diff --git a/homework/m5_visualization.py b/homework/m5_visualization.py
index 7e7335d..f0e5888 100644
--- a/homework/m5_visualization.py
+++ b/homework/m5_visualization.py
@@ -5,6 +5,8 @@
 
 資料路徑：datasets/ecommerce/orders_enriched.csv
 """
+from itertools import groupby
+
 import matplotlib
 matplotlib.use("Agg")  # 無 GUI 環境也能跑
 import matplotlib.pyplot as plt
@@ -29,7 +31,17 @@ def green_bar_category():
     提示：sns.countplot 或 value_counts().plot.bar()
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    plt.figure(figsize=(8, 4))
+
+    sns.countplot(data=df, x='category', color='steelblue')
+    plt.title('Order Count by Category', fontweight='bold')
+    plt.xlabel('Category')
+    plt.ylabel('Order Count')
+    plt.tight_layout()
+    # plt.show()
+    fig = plt.gcf()
+    return fig
 
 
 def green_hist_amount():
@@ -39,8 +51,14 @@ def green_hist_amount():
     提示：sns.histplot(bins=20) 或 plt.hist()
     """
     # TODO: 你的程式碼
-    pass
-
+    df = _load_data()
+    plt.figure(figsize=(8, 4))
+    sns.histplot(data= df, x= 'amount', kde=True, bins=20)
+    plt.xlabel('amount')
+    plt.tight_layout()
+    # plt.show()
+    fig = plt.gcf()
+    return fig
 
 def green_set_labels():
     """
@@ -51,7 +69,17 @@ def green_set_labels():
     回傳 matplotlib Figure 物件
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    plt.figure(figsize=(8, 4))
+
+    sns.countplot(data=df, x='category', color='steelblue')
+    plt.title('Order Count by Category', fontweight='bold')
+    plt.xlabel('Category')
+    plt.ylabel('Order Count')
+    plt.tight_layout()
+    # plt.show()
+    fig = plt.gcf()
+    return fig
 
 
 # ============================================================
@@ -68,7 +96,22 @@ def yellow_line_region_trend():
     提示：分別 groupby 再 plot，或用 sns.lineplot(hue='region')
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    df = df[df['region'].isin(['North', 'South'])]
+    df['month'] = df['order_date'].dt.month
+    df = df.groupby(['region', 'month'])['amount'].sum().reset_index()
+    # print(df)
+    plt.figure(figsize=(8, 4))
+    sns.lineplot(data=df, x='month', y='amount', hue='region', marker='o', linewidth=2)
+    plt.title('Monthly Revenue: North vs South', fontweight='bold')
+    plt.xlabel('Month')
+    plt.ylabel('Amount')
+    plt.legend(title='Region', loc='upper right')
+    plt.tight_layout()
+    # plt.show()
+
+    fig = plt.gcf()
+    return fig
 
 
 def yellow_box_vip():
@@ -78,8 +121,18 @@ def yellow_box_vip():
     提示：sns.boxplot(x='vip_level', y='amount', data=df)
     """
     # TODO: 你的程式碼
-    pass
-
+    df = _load_data()
+    # print(df.head())
+    # df = df.groupby('vip_level')['amount'].reset_index()
+    plt.figure(figsize=(8, 4))
+    sns.boxplot(x='vip_level', y='amount', data=df)
+    plt.title('VIP Level Distribution', fontweight='bold')
+    plt.xlabel('VIP Level')
+    plt.ylabel('Amount')
+    # plt.tight_layout()
+    # plt.show()
+    fig = plt.gcf()
+    return fig
 
 def yellow_scatter_price_amount():
     """
@@ -88,8 +141,26 @@ def yellow_scatter_price_amount():
     提示：plt.scatter() 或 sns.scatterplot()
     """
     # TODO: 你的程式碼
-    pass
-
+    df = _load_data()
+    # print(df.head())
+    plt.figure(figsize=(8, 4))
+    sns.scatterplot(data=df, x='unit_price', y='amount')
+    plt.title('Price Amount by Unit', fontweight='bold')
+    plt.xlabel('Unit')
+    plt.ylabel('Amount')
+    plt.tight_layout()
+    # plt.show()
+    fig = plt.gcf()
+    return fig
+
+# tmp = _load_data()
+# tmp = green_bar_category()
+# tmp = green_hist_amount()
+# tmp = green_set_labels()
+# tmp = yellow_line_region_trend()
+# tmp = yellow_box_vip()
+# tmp = yellow_scatter_price_amount()
+# print(tmp)
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -107,4 +178,38 @@ def red_category_dashboard(category="Electronics"):
     提示：fig, axes = plt.subplots(2, 2, figsize=(14, 10))
     """
     # TODO: 你的程式碼
-    pass
+    df = _load_data()
+    df['month'] = df['order_date'].dt.month
+    df = df[df['category'] == category]
+    # print(df.head())
+    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
+
+    df1 = df.groupby('month')['amount'].sum().reset_index()
+    sns.lineplot(data=df1, x='month', y='amount', ax=axes[0, 0])
+    axes[0, 0].set_title(f'Monthly Revenue: {category}', fontweight='bold')
+    axes[0, 0].set_xlabel('Month')
+    axes[0, 0].set_ylabel('Amount')
+
+    df2 = df.groupby('region')['amount'].sum().reset_index()
+    sns.barplot(data=df2, x='region', y='amount', color='steelblue', ax= axes[0, 1])
+    axes[0, 1].set_title('Amount by Region', fontweight='bold')
+    axes[0, 1].set_xlabel('Region')
+    axes[0, 1].set_ylabel('Amount')
+
+    df3 = df.groupby('product_name')['amount'].sum().sort_values(ascending=False).head(5).reset_index()
+    sns.barplot(data=df3, y='product_name', x='amount', color='steelblue',ax= axes[1, 0])
+    axes[1, 0].set_title(f'Top 5 Amount by Product in {category}', fontweight='bold')
+    axes[1, 0].set_xlabel('Amount')
+    axes[1, 0].set_ylabel('Product')
+
+    # print(df['amount'])
+    sns.histplot(data=df['amount'], bins=30, ax=axes[1, 1])
+    axes[1, 1].set_title(f'Money Distribution by {category}', fontweight='bold')
+    axes[1, 1].set_xlabel('Product')
+    axes[1, 1].set_ylabel('Amount')
+
+    fig.tight_layout()
+    # plt.show()
+    return fig
+
+# red_category_dashboard()
\ No newline at end of file
diff --git a/homework/m6_plotly_capstone.py b/homework/m6_plotly_capstone.py
index 0e2c32a..c57fcc9 100644
--- a/homework/m6_plotly_capstone.py
+++ b/homework/m6_plotly_capstone.py
@@ -26,7 +26,12 @@ def green_plotly_bar():
     提示：px.bar()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv('datasets/ecommerce/orders_enriched.csv')
+    # print(df.head())
+    df = df.groupby('category')['amount'].sum().reset_index()
+    fig = px.bar(df, x='category', y='amount')
+    # fig.show()
+    return fig
 
 
 def green_plotly_line():
@@ -37,7 +42,14 @@ def green_plotly_line():
     提示：先 groupby 月份算總營收，再 px.line()
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv('datasets/ecommerce/orders_enriched.csv')
+    df['order_date'] = pd.to_datetime(df['order_date'])
+    df['month'] = df['order_date'].dt.to_period('M').astype(str)
+    df = df.groupby('month')['amount'].sum().reset_index()
+    fig = px.line(df, x='month', y='amount', markers=True, title='Monthly Revenue')
+    fig.update_layout(height=400)
+    # fig.show()
+    return fig
 
 
 def green_plotly_pie():
@@ -48,8 +60,21 @@ def green_plotly_pie():
     提示：px.pie()
     """
     # TODO: 你的程式碼
-    pass
-
+    df = pd.read_csv('datasets/ecommerce/orders_enriched.csv')
+    # df = df['vip_level'].value_counts().reset_index()
+    df = df.groupby('vip_level')['order_id'].count().reset_index()
+    print(df)
+    fig = px.pie(df, names='vip_level', values='order_id',
+                 title='Number of Orders by VIP Level', hole=0.4)
+    fig.update_layout(height=400)
+    # fig.show()
+    # fig.write_html('first_figure.html', auto_open=True)
+    return fig
+
+
+# green_plotly_bar()
+# green_plotly_line()
+# green_plotly_pie()
 
 # ============================================================
 # 🟡 核心題（每題 15 分，共 45 分）
@@ -63,7 +88,30 @@ def yellow_clean_and_merge(raw_path, customers_path, products_path):
     回傳：合併後的 DataFrame
     """
     # TODO: 你的程式碼
-    pass
+    df = pd.read_csv(raw_path)
+    df_customers = pd.read_csv(customers_path)
+    df_products = pd.read_csv(products_path)
+
+    df.columns = df.columns.str.strip().str.lower()
+    df['amount'] = df['amount'].str.replace('$', '').str.replace(',', '').astype(float)
+    df['order_date'] = pd.to_datetime(df['order_date'])
+    df = df.dropna(subset=['amount', 'order_date'])
+    df = df.drop_duplicates()
+
+    df = (
+        df
+        .merge(df_customers, how='left', on='customer_id')
+        .merge(df_products, how='left', on='product_id')
+    )
+
+    df['qty'] = df['qty'].fillna(df['amount']/df['unit_price']).astype(float)
+    # print(df.head())
+    # print(df.info())
+    # print(df_customers.head())
+    # print(df_products.head())
+    # print(df_customers.info())
+    # print(df_products.info())
+    return df
 
 
 def yellow_kpi_summary(df):
@@ -77,7 +125,14 @@ def yellow_kpi_summary(df):
     }
     """
     # TODO: 你的程式碼
-    pass
+    kpi = {}
+    kpi['total_revenue'] = df['amount'].sum().item()
+    kpi['order_count'] = df['order_id'].count().item()
+    kpi['active_customers'] = df['customer_id'].nunique()
+    kpi['avg_order_value'] = df['amount'].mean().item()
+
+    # print(kpi)
+    return kpi
 
 
 def yellow_plotly_scatter(df):
@@ -91,8 +146,30 @@ def yellow_plotly_scatter(df):
     提示：px.scatter(hover_data=['product_name'])
     """
     # TODO: 你的程式碼
-    pass
-
+    item = df[['product_name', 'unit_price', 'amount', 'category']]
+    # print(item)
+    fig = px.scatter(
+        item,
+        x='unit_price',
+        y='amount',
+        color='category',
+        hover_name='product_name',
+        hover_data={
+            'unit_price': False,
+            'amount': False,
+            'category': False
+        }
+    )
+
+    fig.update_layout(height=400)
+    # fig.write_html('first_figure.html', auto_open=True)
+    return fig
+
+
+
+# temp = yellow_clean_and_merge('../datasets/ecommerce/orders_raw.csv', '../datasets/ecommerce/customers.csv', '../datasets/ecommerce/products.csv')
+# tmp = yellow_kpi_summary(temp)
+# tmp = yellow_plotly_scatter(temp)
 
 # ============================================================
 # 🔴 挑戰題（25 分）
@@ -115,4 +192,49 @@ def red_dashboard():
     提示：from plotly.subplots import make_subplots
     """
     # TODO: 你的程式碼
-    pass
+    df = yellow_clean_and_merge('datasets/ecommerce/orders_raw.csv',
+                                'datasets/ecommerce/customers.csv',
+                                'datasets/ecommerce/products.csv')
+
+
+    df['month'] = df['order_date'].dt.to_period('M').astype(str)
+    df1 = df.groupby('month')['amount'].sum().reset_index()
+    fig1 = px.line(df1, x='month', y='amount', markers=True, title='Monthly Revenue Trend')
+
+    df2 = df.groupby('product_name')['amount'].sum().sort_values(ascending=False).head(10).reset_index()
+    fig2 = px.bar(df2, x='product_name', y='amount', title='Top 10 Revenue of Products')
+
+    df3 = df.groupby('region')['amount'].sum().reset_index()
+    fig3 = px.bar(df3, x='region', y='amount', title='Revenue of Regions')
+
+    df4 = df.groupby('category')['amount'].sum().reset_index()
+    df4['percentage'] = df4['amount'] / df4['amount'].sum() * 100
+    fig4 = px.pie(
+        df4,
+        names='category',
+        values='percentage',
+        title='Percentage of Revenue by Category'
+    )
+
+
+    fig = make_subplots(rows=2, cols=2,
+                        specs=[
+                            [{'type': 'xy'}, {'type': 'xy'}],
+                            [{'type': 'xy'}, {'type': 'domain'}]
+                        ],
+                        subplot_titles=(
+                            'Monthly Revenue Trend',
+                            'Top 10 Revenue of Products',
+                            'Revenue of Regions',
+                            'Percentage of Revenue by Category'
+                        ))
+    fig.add_trace(fig1.data[0], row=1, col=1)
+    fig.add_trace(fig2.data[0], row=1, col=2)
+    fig.add_trace(fig3.data[0], row=2, col=1)
+    fig.add_trace(fig4.data[0], row=2, col=2)
+
+    fig.update_layout(title='All Data Dashboard', height=800)
+    # fig.write_html('first_figure.html', auto_open=True)
+    return fig
+
+# red_dashboard()
\ No newline at end of file